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We consider a region containing several hospitals collaborating to treatpatients during an infectious outbreak. Collaboration occurs through thedynamic allocation of hospital bed capacity to infectious patients and byassigning infectious patients to specific hospitals. Scaling up capacity forinfectious patients means that beds are removed from regular care, which canonly be done simultaneously for all beds in a room. Moreover, as opening roomsfor infectious patients takes some lead time, we have to decide on preparing toopen rooms for infectious patients ahead of time. We apply a stochastic directlookahead approach. Each day, we make decisions on room allocation and patientassignment based on the solutions of two stochastic programs with scenariosusing short-time forecasts of the number of infectious hospitalizations in theregion and the bed occupancy in each collaborating hospital. We aim to balancecosts for bed shortages and unutilized beds for infectious patients and openingand closing rooms. We demonstrate that a stochastic lookahead is superior to adeterministic one. We furthermore compare our solution approach with twoheuristic strategies in a simulation study based on historical COVID-19 data ofa region with three hospitals in the Netherlands. In one strategy, hospitalsdecide on their capacity allocation individually. In another strategy, weassume a pandemic unit, where one hospital is designated to take all regionalinfectious patients until full. The numerical results show that our stochasticdirect lookahead approach considerably outperforms these heuristics.

A stochastic programming approach for dynamic allocation of bed capacity  and assignment of patients to collaborating hospitals during pandemic  outbreaks